Frederick Delay, LHyGeS, University of Strasbourg, France
Philippe Renard, University of Neuchâtel, Switzerland
Daniel Tartakovsky, Stanford University, US
Velimir Vesselinov, Los Alamos National Laboratory, US
Eric Laloy, Belgian Nuclear Research Centre, SCK-CEN, Belgium
Parameter estimation and uncertainty quantification are two key features of modern science-based predictions. When applied to water resources, these tasks have to be able to cope with many degrees of freedom and large datasets. Both are challenging and require novel theoretical and computational approaches to handle complex models with large number of unknown parameters. Our session targets discussions related to novel theoretical and computational methods for parameter estimation and uncertainty quantification with a special focus on big datasets, large-scale inverse models, and their applications. As data quality and information content are also key to efficient model inversion, data analyses and parametric sensitivity analyses of high-dimensional models are also in the scope.